Full Code of DonsetPG/narya for AI

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Repository: DonsetPG/narya
Branch: master
Commit: a26438e9dae0
Files: 90
Total size: 25.2 MB

Directory structure:
gitextract_efwv_cne/

├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── Liverpool-LastRow/
│   │   ├── LIV_[2]-1_CHE.csv
│   │   ├── Real_vs_Barcelona_Calma.csv
│   │   └── liverpool_2019.csv
│   └── liverpool_2019.csv
├── data-analysis.ipynb
├── full-tracking.ipynb
├── html/
│   └── narya/
│       ├── analytics/
│       │   ├── edg_agent.html
│       │   └── index.html
│       ├── datasets/
│       │   ├── homography_dataset.html
│       │   ├── index.html
│       │   ├── keypoints_dataset.html
│       │   └── tracking_dataset.html
│       ├── index.html
│       ├── linker/
│       │   ├── basetrack.html
│       │   ├── index.html
│       │   ├── kalman_filter.html
│       │   ├── matching.html
│       │   └── multitracker.html
│       ├── models/
│       │   ├── gluon_models.html
│       │   ├── index.html
│       │   ├── keras_layers.html
│       │   ├── keras_models.html
│       │   ├── torch_layers.html
│       │   └── torch_models.html
│       ├── preprocessing/
│       │   ├── image.html
│       │   └── index.html
│       ├── tracker/
│       │   ├── full_tracker.html
│       │   ├── homography_estimator.html
│       │   ├── index.html
│       │   └── player_ball_tracker.html
│       ├── trainer/
│       │   ├── homography_train.html
│       │   ├── index.html
│       │   ├── keypoints_train.html
│       │   └── tracker_train.html
│       └── utils/
│           ├── data.html
│           ├── google_football_utils.html
│           ├── homography.html
│           ├── image.html
│           ├── index.html
│           ├── linker.html
│           ├── masks.html
│           ├── tracker.html
│           ├── utils.html
│           └── vizualization.html
├── models_examples.ipynb
├── narya/
│   ├── README.md
│   ├── __init__.py
│   ├── analytics/
│   │   ├── __init__.py
│   │   └── edg_agent.py
│   ├── datasets/
│   │   ├── __init__.py
│   │   ├── homography_dataset.py
│   │   ├── keypoints_dataset.py
│   │   └── tracking_dataset.py
│   ├── linker/
│   │   ├── __init__.py
│   │   ├── basetrack.py
│   │   ├── kalman_filter.py
│   │   ├── matching.py
│   │   └── multitracker.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── gluon_models.py
│   │   ├── keras_layers.py
│   │   ├── keras_models.py
│   │   ├── torch_layers.py
│   │   └── torch_models.py
│   ├── preprocessing/
│   │   ├── __init__.py
│   │   └── image.py
│   ├── requirements.txt
│   ├── tracker/
│   │   ├── __init__.py
│   │   ├── full_tracker.py
│   │   ├── homography_estimator.py
│   │   └── player_ball_tracker.py
│   ├── trainer/
│   │   ├── __init__.py
│   │   ├── homography_train.py
│   │   ├── keypoints_train.py
│   │   └── tracker_train.py
│   └── utils/
│       ├── __init__.py
│       ├── data.py
│       ├── google_football_utils.py
│       ├── homography.py
│       ├── image.py
│       ├── linker.py
│       ├── masks.py
│       ├── tracker.py
│       ├── utils.py
│       └── vizualization.py
├── requirements.txt
└── training.ipynb

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*.DS_Store
*.pyc
*.swp
temp/*
dist/*
build/*
*.__pycache__
*.ipynb_checkpoints
*.dump

# test-related
.coverage
.cache
.pytest_cache

# developer environments
.idea
.vscode


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2020 Paul Garnier

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# Narya

The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository contains the implementation of the [following paper](https://arxiv.org/abs/2101.05388). We also make available all of our pretrained agents, and the datasets we used as well. 

The goal of this repository is to allow anyone without any access to soccer data to produce its own and to analyse them with powerfull tools. We also hope that by releasing our training procedures and datasets, better models will emerge and make this tool better iteratively. 

We also built 4 notebooks to explain how to use our models and a colab:

- [Colab](https://colab.research.google.com/drive/1VC3yd_M4va86N0q9NsYT0ajhCZ-k1sBO?usp=sharing)
- [Each tracking model](https://github.com/DonsetPG/narya/blob/master/models_examples.ipynb)
- [Complete Tracker](https://github.com/DonsetPG/narya/blob/master/full-tracking.ipynb)
- [EDG DRL Agent](https://github.com/DonsetPG/narya/blob/master/data-analysis.ipynb)
- [Datasets and Training](https://github.com/DonsetPG/narya/blob/master/training.ipynb)

and released of blog post version of these notebooks [here](https://donsetpg.github.io/blog/2020/12/24/Narya/).

We tried to make everything easy to reuse, we hope anyone will be able to:

* Use our datasets to train other models
* Finetune some of our trained models
* Use our trackers
* Evaluate players with our EDG Agent
* and much more

You can find at the bottom of the readme links to our models and datasets, but also to tools and models trained by the community. 

# Installation  

You can either install narya from source:

```git clone && cd narya && pip3 install -r requirements.txt```

### Google Football: 

Google Football needs to be installed differently. Please see their repo to take care of it.

[Google Football Repo](https://github.com/google-research/football)

## Player tracking: 

The installed version is directly compatible with the player tracking models. However, it seems that some errors might occur with ```keras.load_model``` when the architecture of the model is contained in the .h5 file. In doubt, Python 3.7 is always working with our installation.

## EDG: 

As Google Football API is currently not supporting Tensorflow 2, you need to manually downgrade its version in order to use our EDG agent: 

```pip3 install tensorflow==1.13.1```
```pip3 install tensorflow_probability==0.5.0```

### Models & Datasets:

The models will be downloaded automatically with the library. If needed, they can be access at the end of the readme. The datasets are also available below.

# Tracking Players Models:

Each model can be accessed on its own, or you can use the full tracking itself.

## Single Model 

Each pretrained model is built on the same architecture to allow for the easier utilisation possible: you import it, and you use it. The processing function, or different frameworks, are handled internaly.

Let's import an image:

```
import numpy as np
import cv2
image = cv2.imread('test_image.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
```

Now, let's create our models:

```python3
from narya.models.keras_models import DeepHomoModel
from narya.models.keras_models import KeypointDetectorModel
from narya.models.gluon_models import TrackerModel

direct_homography_model = DeepHomoModel()

keypoint_model = KeypointDetectorModel(
    backbone='efficientnetb3', num_classes=29, input_shape=(320, 320),
)

tracking_model = TrackerModel(pretrained=True, backbone='ssd_512_resnet50_v1_coco')
```

We can now directly make predictions: 

```
homography_1 = direct_homography_model(image)
keypoints_masks = keypoint_model(image)
cid, score, bbox = tracking_model(image)
```

In the tracking class, we also process the homography we estimate with interpolation and filters. This ensure smooth estimation during the entire video.

### Processing: 

We can now vizualise or use each of this predictions.
For example, visualize the predicted keypoints: 

```
from narya.utils.vizualization import visualize
visualize(
        image=denormalize(image.squeeze()),
        pr_mask=keypoints_masks[..., -1].squeeze(),
    )
```

## Full Tracker: 

Given a list of images, one can easily apply our tracking algorithm: 

```
from narya.tracker.full_tracker import FootballTracker
```

This tracker contains the 3 models seen above, and the tracking/ReIdentification model. 
You can create it by specifying your frame rate, and the size of the memory frames buffer:

```
tracker = FootballTracker(frame_rate=24.7,track_buffer = 60)
```

Given a list of image, the full tracking is computed using:

```
trajectories = tracker(img_list,split_size = 512, save_tracking_folder = 'test_tracking/',
                        template = template,skip_homo = None)
```

We also built post processing functions to handle the mistakes the tracker can make, and also visualization tools to plot the data.

# EDG:

The best way to use our EDG agent is to first convert your tracking data to a google format, using the utils functions: 

```python3
from narya.utils.google_football_utils import _save_data, _build_obs_stacked

data_google = _save_data(df,'test_temo.dump')
observations = {
    'frame_count':[],
    'obs':[],
    'obs_count':[],
    'value':[]
}
for i in range(len(data_google)):
    obs,obs_count = _build_obs_stacked(data_google,i)
    observations['frame_count'].append(i)
    observations['obs'].append(obs)
    observations['obs_count'].append(obs_count)
```

You can now easily load a pretrained agent, and use it to get the value of any action with:

```python3
from narya.analytics.edg_agent import AgentValue

agent = AgentValue(checkpoints = checkpoints)
value = agent.get_value([obs])
```

## Processing:

You can use these values to plot the value of an action, or plot map of values at a given time.
You can use: 

```python3 
map_value = agent.get_edg_map(observations['obs'][20],observations['obs_count'][20],79,57,entity = 'ball')
```

and

```python3
for indx,obs in enumerate(observations['obs']):
    value = agent.get_value([obs])
    observations['value'].append(value)
df_dict = {
    'frame_count':observations['frame_count'],
    'value':observations['value']
}
df_ = pd.DataFrame(df_dict)
```

to compute an EDG map and the EDG overtime of an action.

# Open Source

Our goal with this project was to both build a powerful tool to analyse soccer plays. This led us to build a soccer player tracking model on top of it. We hope that by releasing our codes, weights, and datasets, more people will be able to perform amazing projects related to soccer/sport analysis.

If you find any bug, please open an issue. If you see any improvements, or trained a model you want to share, please open a pull request!

# Thanks

A special thanks to [Last Row](https://twitter.com/lastrowview), for providing some tracking data at the beginning, to try our agent, and to [Soccermatics](https://twitter.com/Soccermatics) for providing Vizualisation tools (and some motivation to start this project).

# Citation

If you use Narya in your research and would like to cite it, we suggest you use the following citation:
```
@misc{garnier2021evaluating,
      title={Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning}, 
      author={Paul Garnier and Théophane Gregoir},
      year={2021},
      eprint={2101.05388},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
# Links:

## Links to the models and datasets from the original Paper

| Model                  | Description                               | Link                                                                        |
|------------------------|-------------------------------------------|-----------------------------------------------------------------------------|
| 11_vs_11_selfplay_last | EDG agent                                 | https://storage.googleapis.com/narya-bucket-1/models/11_vs_11_selfplay_last |
| deep_homo_model.h5     | Direct Homography estimation Weights      | https://storage.googleapis.com/narya-bucket-1/models/deep_homo_model.h5     |
| deep_homo_model_1.h5   | Direct Homography estimation Architecture | https://storage.googleapis.com/narya-bucket-1/models/deep_homo_model_1.h5   |
| keypoint_detector.h5   | Keypoints detection Weights               | https://storage.googleapis.com/narya-bucket-1/models/keypoint_detector.h5   |
| player_reid.pth        | Player Embedding Weights                  | https://storage.googleapis.com/narya-bucket-1/models/player_reid.pth        |
| player_tracker.params  | Player & Ball detection Weights           | https://storage.googleapis.com/narya-bucket-1/models/player_tracker.params  |

The datasets can be downloaded at: 

| Dataset                | Description                                                             | Link                                                                         |
|------------------------|-------------------------------------------------------------------------|------------------------------------------------------------------------------|
| homography_dataset.zip | Homography Dataset (image,homography)                                   | https://storage.googleapis.com/narya-bucket-1/dataset/homography_dataset.zip |
| keypoints_dataset.zip  | Keypoint Dataset (image,list of mask)                                   | https://storage.googleapis.com/narya-bucket-1/dataset/keypoints_dataset.zip  |
| tracking_dataset.zip   | Tracking Dataset in VOC format (image, bounding boxes for players/ball) | https://storage.googleapis.com/narya-bucket-1/dataset/tracking_dataset.zip   |

## Links to models trained by the community 

### Experimental data for vertical pitches:

| Model                                    | Description                               | Link                                                                                            |
|------------------------------------------|-------------------------------------------|-------------------------------------------------------------------------------------------------|
| vertical_HomographyModel_0.0001_32.h5    | Direct Homography estimation Weights      | https://storage.googleapis.com/narya-bucket-1/models/vertical_HomographyModel_0.0001_32.h5      |
| vertical_FPN_efficientnetb3_0.0001_32.h5 | Keypoints detection Weights               | https://storage.googleapis.com/narya-bucket-1/models/vertical_FPN_efficientnetb3_0.0001_32.h5   |

| Dataset                                | Description                                                             | Link                                                                                        |
|----------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| vertical_samples_direct_homography.zip | Homography Dataset (image,homography)                                   | https://storage.googleapis.com/narya-bucket-1/dataset/vertical_samples_direct_homography.zip|
| vertical_samples_keypoints.zip         | Keypoint Dataset (image,list of mask)                                   | https://storage.googleapis.com/narya-bucket-1/dataset/vertical_samples_keypoints.zip        |

# Tools

## Tool for efficient creation of training labels:

Tool built by [@larsmaurath](https://github.com/larsmaurath) to label football images: https://github.com/larsmaurath/narya-label-creator

## Tool for creation of keypoints datasets:

Tool built by [@kkoripl](https://github.com/kkoripl) to create keypoints datasets - xml files and images resizing: https://github.com/kkoripl/NaryaKeyPointsDatasetCreator


================================================
FILE: data/Liverpool-LastRow/LIV_[2]-1_CHE.csv
================================================
player,frame,x,y,z,dx,dy,coords,player_num,player_obj,team,num,name,edgecolor,bgcolor
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12,12,37.403923502793205,90.73239205988374,,0.062329896852780564,0.1521726808184667,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,13,37.4648421017921,90.8820710603413,,0.060918598998895845,0.1496790004575672,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,14,37.52387858879324,91.02901925331122,,0.05903648700113706,0.14694819296991568,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,15,37.58056214965269,91.17299951166675,,0.056683560859454474,0.1439802583555263,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,16,37.63442197022656,91.31377470828116,,0.0538598205738694,0.1407751966144133,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,17,37.68498723637093,91.45110771602774,,0.05056526614436763,0.13733300774657664,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,18,37.731787133941886,91.58476140777972,,0.04679989757095626,0.13365369175198794,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,19,37.77435084879553,91.71449865641038,,0.04256371485364241,0.12973724863066138,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,20,37.81220756678795,91.840082334793,,0.037856717992418965,0.1255836783826112,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,21,37.84488647377523,91.96127531580082,,0.03267890698728593,0.12119298100782316,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,22,37.87191675561347,92.07784047230713,,0.027030281838236192,0.1165651565063115,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,23,37.89282759815874,92.18954067718518,,0.02091084254526976,0.11170020487804777,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,24,37.90714818726716,92.29613880330822,,0.014320589108422155,0.1065981261230462,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,25,37.9144077087948,92.39739772354956,,0.007259521527636537,0.1012589202413352,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,26,37.91413534859776,92.49308031078243,,-0.0002723601970373579,0.09568258723287215,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,27,37.90586029253212,92.5829494378801,,-0.008275056065642161,0.08986912709767125,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,28,37.889111726454,92.66676797771584,,-0.01674856607812103,0.08381853983573251,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,29,37.8635263567206,92.7443593908559,,-0.025585369733398977,0.07759141314006968,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,30,37.8291709716938,92.81578948863849,,-0.03435538502679947,0.07143009778258147,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,31,37.786219880236594,92.88118467009475,,-0.04295109145720488,0.06539518145626744,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,32,37.734847391212,92.94067133425587,,-0.051372489024593904,0.05948666416111337,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,33,37.67522781348301,92.99437588015302,,-0.05961957772898785,0.05370454589714768,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,34,37.607535455912625,93.04242470681739,,-0.06769235757038672,0.04804882666437038,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,35,37.53194462736387,93.08494421328012,,-0.07559082854875498,0.04251950646273883,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,36,37.448629636699714,93.12206079857243,,-0.0833149906641566,0.03711658529230988,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,37,37.3577647927832,93.15390086172546,,-0.0908648439165134,0.031840063153026676,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,38,37.259524404477304,93.1805908017704,,-0.09824038830589643,0.02668994004494607,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,39,37.15408278064505,93.20225701773842,,-0.10544162383225597,0.021666215968011215,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,40,37.041614230149435,93.2190259086607,,-0.11246855049561333,0.016768890922278956,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,41,36.922293061853466,93.2310238735684,,-0.1193211682959685,0.01199796490770666,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,42,36.79629358462015,93.23837731149271,,-0.1259994772333144,0.007353437924308537,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,43,36.663790107312465,93.2412126214648,,-0.13250347730768652,0.0028353099720845876,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,44,36.524956938793466,93.23965620251583,,-0.13883316851899963,-0.001556418948965188,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,45,36.37996838792611,93.233834453677,,-0.14498855086735318,-0.005821748838826579,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,46,36.22899876357341,93.22387377397946,,-0.15096962435270456,-0.009960679697542218,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,47,36.07222237459841,93.20990056245441,,-0.1567763889749969,-0.013973211525055262,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,48,35.90981352986406,93.192041218133,,-0.16240884473435102,-0.017859344321408344,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,49,35.7419465382334,93.17042214004643,,-0.16786699163066032,-0.02161907808657304,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,50,35.568795708569425,93.14516972722583,,-0.17315082966397455,-0.025252412820591985,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,51,35.390535349735146,93.11641037870243,,-0.1782603588342795,-0.028759348523408335,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,52,35.207339770593556,93.08427049350736,,-0.18319557914158935,-0.03213988519506472,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,53,35.01938328000766,93.04887647067183,,-0.18795649058589703,-0.035394022835532724,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,54,34.82684018684047,93.01035470922699,,-0.19254309316718832,-0.03852176144484076,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,55,34.62988479995497,92.96883160820401,,-0.19695538688549874,-0.04152310102297463,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,56,34.42869142821421,92.9244335666341,,-0.20119337174076435,-0.04439804156992011,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,57,34.22343438048115,92.87728698354839,,-0.2052570477330562,-0.04714658308570563,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,58,34.01428796561882,92.82751825797807,,-0.20914641486233165,-0.049768725570316974,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,59,33.801426705850076,92.77517694209988,,-0.2128612597687436,-0.052341315878194905,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,60,33.585025976837294,92.72000520067267,,-0.21640072901278273,-0.05517174142720194,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,61,33.365261367602734,92.6616683516009,,-0.21976460923455932,-0.05833684907177883,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,62,33.1423084671686,92.59983171278897,,-0.2229529004341373,-0.06183663881192558,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,63,32.916342864557144,92.53416060214133,,-0.2259656026114527,-0.0656711106476422,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,64,32.6875401487906,92.4643203375624,,-0.2288027157665411,-0.06984026457892867,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,65,32.45607590889122,92.38997623695661,,-0.23146423989938114,-0.074344100605785,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,66,32.22212573388122,92.31079361822839,,-0.23395017501000126,-0.0791826187282254,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,67,31.985865212782873,92.22643779928215,,-0.23626052109834816,-0.08435581894623567,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,68,31.747469934618387,92.13657409802235,,-0.2383952781644858,-0.08986370125980159,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,69,31.507115488410008,92.0408678323534,,-0.24035444620837865,-0.09570626566895157,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,70,31.26497746317997,91.93898432017971,,-0.24213802523003736,-0.10188351217368563,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,71,31.021231447950505,91.83058887940575,,-0.24374601522946548,-0.10839544077396113,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,72,30.77605303174388,91.71534682793593,,-0.24517841620662395,-0.1152420514698207,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,73,30.52961780358231,91.59292348367467,,-0.24643522816157315,-0.12242334426126433,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,74,30.282101352488034,91.46298416452639,,-0.247516451094274,-0.12993931914827783,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,75,30.033679267483297,91.32519418839554,,-0.24842208500473717,-0.13778997613084698,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,76,29.784527137590345,91.17921887318654,,-0.249152129892952,-0.1459753152090002,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,77,29.53482055183139,91.02472353680382,,-0.249706585758954,-0.15449533638272328,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,78,29.284735099228694,90.8613734971518,,-0.250085452602697,-0.16335003965201622,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,79,29.034446368804502,90.68883407213492,,-0.25028873042419164,-0.17253942501687902,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,80,28.784129949581004,90.50677057965761,,-0.25031641922349834,-0.18206349247731168,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,81,28.53396143058049,90.31484833762428,,-0.25016851900051407,-0.19192224203332842,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,82,28.284116400825194,90.11273266393937,,-0.24984502975529566,-0.202115673684915,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,83,28.03474673604126,89.90019146167826,,-0.2493696647839343,-0.2125412022611073,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,84,27.785909458770597,89.67740297460007,,-0.2488372772706633,-0.22278848707819066,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,85,27.53763787825905,89.44464803163488,,-0.2482715805115454,-0.23275494296518673,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,86,27.289965303752457,89.20220746171279,,-0.24767257450659486,-0.2424405699220955,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,87,27.04292504449664,88.95036209376387,,-0.2470402592558152,-0.251845367948917,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,88,26.796550409737435,88.68939275671819,,-0.2463746347592064,-0.2609693370456796,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,89,26.550874708720695,88.41958027950585,,-0.24567570101674008,-0.2698124772123407,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,90,26.305931250692232,88.14120549105691,,-0.2449434580284624,-0.2783747884489429,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,91,26.06175334489788,87.85454922030146,,-0.24417790579435206,-0.28665627075544364,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,92,25.818374300583493,87.5598922961696,,-0.24337904431438773,-0.2946569241318713,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,93,25.575827426994895,87.25751554759137,,-0.24254687358859783,-0.30237674857822583,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,94,25.33414603337792,86.94769980349687,,-0.24168139361697527,-0.3098157440944931,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,95,25.09336342897842,86.63072589281619,,-0.24078260439949872,-0.31697391068068725,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,96,24.8535129230422,86.30687464447941,,-0.23985050593622148,-0.3238512483367799,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,97,24.614627824815106,85.9764268874166,,-0.2388850982270938,-0.3304477570628137,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,98,24.376741443542997,85.63966345055782,,-0.2378863812721086,-0.3367634368587744,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,99,24.139887088471664,85.2968651628332,,-0.23685435507133334,-0.3427982877246194,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,100,23.904098068846977,84.94831285317278,,-0.23578901962468635,-0.3485523096604197,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,101,23.66940769391476,84.59428735050665,,-0.23469037493221734,-0.35402550266613275,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,102,23.43584927292084,84.2350694837649,,-0.2335584209939192,-0.3592178667417443,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,103,23.20345611511106,83.87094008187762,,-0.23239315780978131,-0.3641294018872827,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,104,22.97226152973126,83.50217997377484,,-0.2311945853798001,-0.3687601081027765,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,105,22.742298826027266,83.1290699883867,,-0.2299627037039933,-0.37310998538814033,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,106,22.51360131324491,82.75189095464326,,-0.2286975127823574,-0.3771790337434453,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,107,22.286202300630034,82.37092370147458,,-0.2273990126148746,-0.3809672531686772,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,108,22.060135097428475,81.98644905781076,,-0.22606720320155915,-0.3844746436638218,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,109,21.835433012886053,81.59874785258188,,-0.2247020845424217,-0.3877012052288791,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,110,21.61212935624863,81.20810091471802,,-0.22330365663742313,-0.3906469378638633,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,111,21.390257436762017,80.81478907314924,,-0.22187191948661322,-0.3933118415687744,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,112,21.16985056367205,80.41909315680564,,-0.2204068730899671,-0.39569591634359824,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,113,20.950942046224572,80.02129399461731,,-0.21890851744747764,-0.3977991621883348,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,114,20.733565193665417,79.62167241551433,,-0.2173768525591555,-0.399621579102984,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,115,20.517753315240412,79.22050924842674,,-0.21581187842500427,-0.4011631670875886,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,116,20.303539720195417,78.81808532228466,,-0.2142135950449955,-0.40242392614207745,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,117,20.090957717776227,78.41468146601817,,-0.21258200241918956,-0.4034038562664932,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,118,19.88004061722871,78.01057850855733,,-0.210917100547519,-0.4041029574608359,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,119,19.670821727798675,77.60605727883222,,-0.20921888943003353,-0.4045212297251055,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,120,19.463334358731984,77.20139860577295,,-0.20748736906669052,-0.4046586730592736,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,121,19.257611819274466,76.79688331830957,,-0.2057225394575184,-0.40451528746338283,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,122,19.05368741867192,76.39279224537216,,-0.20392440060254557,-0.40409107293740476,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,123,18.851594466170233,75.98940621589082,,-0.2020929525016868,-0.4033860294813394,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,124,18.65136627101521,75.58700605879562,,-0.20022819515502377,-0.40240015709520094,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,125,18.45299149071732,75.18580409353058,,-0.19837478029788969,-0.40120196526504515,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,126,18.256280175845585,74.78573860159545,,-0.19671131487173454,-0.4000654919351234,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,127,18.06099772523365,74.3866793550039,,-0.19528245061193417,-0.39905924659154834,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,128,17.866909537715163,73.98849612576964,,-0.19408818751848855,-0.3981832292342631,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,129,17.67378101212376,73.59105868590635,,-0.19312852559140126,-0.39743743986329605,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,130,17.481377547293107,73.19423680742769,,-0.1924034648306545,-0.39682187847866146,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,131,17.289464542056834,72.79790026234738,,-0.1919130052362732,-0.39633654508030247,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,132,17.0978073952486,72.40191882267908,,-0.19165714680823243,-0.39598143966830435,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,133,16.90617150570204,72.0061622604365,,-0.19163588954656063,-0.3957565622425818,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,134,16.714322272250815,71.61050034763329,,-0.19184923345122584,-0.39566191280320595,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,135,16.522025093728555,71.21480285628317,,-0.19229717852226003,-0.3956974913501199,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,136,16.329045368968924,70.8189395583998,,-0.1929797247596312,-0.39586329788336627,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,137,16.13514849680556,70.4227802259969,,-0.19389687216336426,-0.39615933240290246,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,138,15.940099876072114,70.02619463108812,,-0.19504862073344498,-0.3965855949087853,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,139,15.743664905602223,69.62905254568716,,-0.1964349704698911,-0.39714208540095797,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,140,15.54560898422955,69.2312237418077,,-0.19805592137267247,-0.39782880387946307,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,141,15.345697510787728,68.83257799146344,,-0.1999114734418228,-0.398645750344258,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,142,15.143695884110416,68.43298506666804,,-0.20200162667731192,-0.39959292479539954,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,143,14.939443069288458,68.03251486229728,,-0.20425281482195778,-0.4004702043707624,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,144,14.733072296441522,67.63203776467515,,-0.2063707728469364,-0.40047709762212946,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,145,14.524790361946513,67.23262428298776,,-0.20828193449500887,-0.39941348168738955,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,146,14.314804062180295,66.8353449264211,,-0.20998629976621785,-0.39727935656665636,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,147,14.10332019351976,66.44127020416134,,-0.21148386866053492,-0.39407472225975937,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,148,13.890545552341782,66.0514706253945,,-0.21277464117797784,-0.3897995787668407,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,149,13.676686935023245,65.66701669930669,,-0.21385861731853772,-0.38445392608781503,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,150,13.461951137941028,65.28897893508395,,-0.21473579708221635,-0.37803776422273927,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,151,13.246544957472015,64.91842784191238,,-0.21540618046901372,-0.37055109317157076,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,152,13.030675189993094,64.55643392897804,,-0.21586976747892095,-0.3619939129343379,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,153,12.814548631881143,64.204067705467,,-0.21612655811195047,-0.35236622351104074,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,154,12.59837207951304,63.86239968056536,,-0.21617655236810407,-0.3416680249016366,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,155,12.382352329265665,63.53250036345921,,-0.21601975024737463,-0.32989931710615394,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,156,12.166696177515908,63.215440263334564,,-0.21565615174975683,-0.3170601001246425,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,157,11.951610420640648,62.91228988937753,,-0.21508575687525955,-0.30315037395703115,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,158,11.737301855016767,62.624119750774184,,-0.214308565623881,-0.2881701386033484,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,159,11.523977277021146,62.35200035671061,,-0.2133245779956212,-0.2721193940635729,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,160,11.311843483030657,62.097002216372864,,-0.21213379399048904,-0.25499814033774726,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,161,11.101107269422197,61.86019583894704,,-0.21073621360845962,-0.23680637742582178,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,162,10.891975432572643,61.642651733619196,,-0.20913183684955428,-0.21754410532784618,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,163,10.684654768858875,61.445440409575426,,-0.20732066371376767,-0.19721132404377073,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,164,10.479352074657774,61.26963237600177,,-0.2053026942011016,-0.17580803357365227,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,165,10.276274146346227,61.11629814208434,,-0.20307792831154714,-0.15333423391743395,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,166,10.075627780301112,60.98650821700919,,-0.200646366045115,-0.1297899250751513,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,167,9.877619772899306,60.88133310996242,,-0.19800800740180513,-0.1051751070467688,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,168,9.682456920517698,60.801843330130076,,-0.1951628523816087,-0.07948977983234329,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,169,9.490346019533174,60.74910938669824,,-0.19211090098452388,-0.052733943431839236,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,170,9.3014938663226,60.72420178885301,,-0.1888521532105738,-0.024907597845228224,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,171,9.116107257262872,60.72819104578042,,-0.18538660905972826,0.0039892569274115885,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,172,8.934392988730863,60.76214766666657,,-0.18171426853200856,0.033956620886151256,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,173,8.756557857103465,60.82714216069755,,-0.17783513162739872,0.06499449403097657,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,174,8.582808658757548,60.92424503705939,,-0.1737491983459165,0.09710287636184489,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,175,8.41335219007,61.054526804938234,,-0.1694564686875477,0.1302817678788415,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,176,8.248395247417706,61.21905797352009,,-0.1649569426522941,0.16453116858185268,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,177,8.088144627177538,61.41890905199106,,-0.16025062024016812,0.19985107847097083,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,178,7.932807125726388,61.65515054953722,,-0.15533750145115022,0.23624149754616042,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,179,7.782589539441134,61.92885297534465,,-0.15021758628525372,0.27370242580742854,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,180,7.637698664698653,62.24108683859941,,-0.1448908747424813,0.312233863254761,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,181,7.498341297875835,62.59292264848758,,-0.13935736682281785,0.351835809888172,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,182,7.364724235349558,62.98543091419525,,-0.1336170625262767,0.3925082657076686,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,183,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,184,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,185,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,186,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,187,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,188,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,189,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,190,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,191,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,192,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,193,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
12,194,7.364724235349558,62.98543091419525,,0.0,0.0,"(54.13072312981925, 176.18934884843065)",10.0,12.0,attack,,,white,red
1214,0,29.88866895104159,71.90668707574757,,0.0,0.0,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,1,29.848445400049112,72.05364520957957,,-0.04022355099247932,0.14695813383200118,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,2,29.8190079697433,72.18975731356421,,-0.02943743030581203,0.1361121039846438,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,3,29.799774831814396,72.31549674959027,,-0.019233137928903687,0.12573943602605198,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,4,29.79016415795269,72.43133687954662,,-0.009610673861704555,0.11584012995635362,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,5,29.7895941198484,72.53775106532204,,-0.0005700381042927916,0.10641418577542083,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,6,29.797482889191805,72.63521266880532,,0.00788876934340621,0.09746160348328203,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,7,29.813248637673155,72.7241950518853,,0.015765748481349817,0.08898238307997985,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,8,29.836309536982707,72.80517157645079,,0.02306089930955224,0.08097652456548587,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,9,29.866083758810735,72.87861560439057,,0.02977422182802769,0.07344402793978588,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,10,29.90198947484748,72.9450004975935,,0.035905716036744195,0.06638489320292251,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,11,29.943444856783202,73.00479961794835,,0.04145538193572307,0.05979912035485313,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,12,29.989868076308163,73.05848632734396,,0.04642321952496076,0.05368670939560616,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,13,30.040677305112613,73.10653398766911,,0.05080922880445016,0.048047660325153174,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,14,30.09529071488683,73.14941596081263,,0.05461340977421614,0.0428819731435226,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,15,30.153126477321035,73.18760560866333,,0.05783576243420541,0.038189647850700226,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,16,30.213602764105524,73.22157629311002,,0.06047628678448902,0.03397068444668605,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,17,30.27613774693053,73.25180137604153,,0.06253498282500658,0.030225082931508496,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,18,30.340149597486317,73.27875421934664,,0.06401185055578651,0.02695284330511072,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,19,30.405056487463145,73.30290818491416,,0.06490688997682881,0.02415396556752114,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,20,30.470276588551286,73.32473663463293,,0.06522010108814058,0.021828449718768184,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
1214,21,30.53522807244096,73.34471293039172,,0.06495148388967564,0.019976295758795004,"(1.7201301895893302, 116.50868087225327)",9.0,1214.0,attack,,,white,red
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3344,69,27.589432065944706,74.31755027329109,,-0.1608296959808584,0.20649050111509837,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,70,27.426113171053924,74.52224934867677,,-0.16331889489078222,0.2046990753856761,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,71,27.260061781378692,74.72401604068916,,-0.16605138967523203,0.2017666920123986,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,72,27.091034601044583,74.92170939168442,,-0.16902718033410835,0.19769335099525165,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,73,26.91878833417712,75.11418844401865,,-0.17224626686746447,0.19247905233423523,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,74,26.743079684901794,75.30031224004803,,-0.17570864927532526,0.18612379602937779,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,75,26.563665357344146,75.47893982212865,,-0.17941432755764808,0.17862758208062246,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,76,26.380302055629727,75.64893023261669,,-0.18336330171441872,0.1699904104880403,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,77,26.19274648388404,75.80914251386825,,-0.18755557174568693,0.16021228125156028,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,78,26.000755346232594,75.9584357082395,,-0.1919911376514456,0.14929319437125343,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,79,25.804085346800953,76.09566885808655,,-0.19666999943164143,0.1372331498470487,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,80,25.602493189714608,76.21970100576557,,-0.20159215708634548,0.12403214767901716,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,81,25.3957355790991,76.32939119363266,,-0.20675761061550801,0.10969018786708773,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,82,25.18356921907996,76.42359846404398,,-0.2121663600191397,0.09420727041131727,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,83,24.96582037776292,76.50144331830414,,-0.21774884131703942,0.07784485426016374,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,84,24.74259357917464,76.56309209351177,,-0.2232267985882821,0.06164877520762957,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,85,24.51406291132198,76.60897258571394,,-0.22853066785265952,0.04588049220217272,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,86,24.280402462211814,76.63951259095775,,-0.2336604491101646,0.0305400052438074,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,87,24.041786319850996,76.6551399052903,,-0.23861614236081863,0.015627314332547826,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,88,23.798388572246402,76.65628232475868,,-0.2433977476045932,0.001142419468379785,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,89,23.550383307404903,76.64336764540997,,-0.24800526484149898,-0.012914679348710933,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,90,23.297944613333357,76.61682366329129,,-0.2524386940715466,-0.026543982118681697,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,91,23.041246578038653,76.5770781744497,,-0.25669803529470414,-0.03974548884158935,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,92,22.78046328952763,76.52455897493232,,-0.2607832885110213,-0.052519199517377047,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,93,22.51576883580718,76.45969386078625,,-0.26469445372045186,-0.06486511414607321,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,94,22.24733730488416,76.38291062805857,,-0.26843153092302074,-0.07678323272767784,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,95,21.97534278476543,76.29463707279636,,-0.27199452011872793,-0.08827355526220515,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,96,21.699959363457864,76.19530099104675,,-0.2753834213075663,-0.0993360817496125,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,97,21.421361128968343,76.0853301788568,,-0.2785982344895217,-0.10997081218995675,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,98,21.139722169303713,75.9651524322736,,-0.2816389596646296,-0.12017774658319524,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,99,20.855216572470862,75.83519554734427,,-0.2845055968328509,-0.129956884929328,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,100,20.568018426476637,75.6958873201159,,-0.2871981459942248,-0.13930822722836922,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,101,20.278301819327922,75.54765554663557,,-0.2897166071487156,-0.14823177348033312,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,102,19.986240839031584,75.39092802295039,,-0.29206098029633765,-0.15672752368517706,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,103,19.692009573594472,75.22613254510743,,-0.2942312654371122,-0.1647954778429579,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,104,19.395782111023475,75.05369690915381,,-0.29622746257099664,-0.17243563595361877,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,105,19.097732539325456,74.8740489111366,,-0.2980495716980194,-0.17964799801721654,"(55.42229087420315, 198.61986626488292)",,3344.0,defense,,,white,blue
3344,106,18.79803494650727,74.68761634710292,,-0.29969759281818753,-0.18643256403368014,"(
Download .txt
gitextract_efwv_cne/

├── .gitignore
├── LICENSE
├── README.md
├── data/
│   ├── Liverpool-LastRow/
│   │   ├── LIV_[2]-1_CHE.csv
│   │   ├── Real_vs_Barcelona_Calma.csv
│   │   └── liverpool_2019.csv
│   └── liverpool_2019.csv
├── data-analysis.ipynb
├── full-tracking.ipynb
├── html/
│   └── narya/
│       ├── analytics/
│       │   ├── edg_agent.html
│       │   └── index.html
│       ├── datasets/
│       │   ├── homography_dataset.html
│       │   ├── index.html
│       │   ├── keypoints_dataset.html
│       │   └── tracking_dataset.html
│       ├── index.html
│       ├── linker/
│       │   ├── basetrack.html
│       │   ├── index.html
│       │   ├── kalman_filter.html
│       │   ├── matching.html
│       │   └── multitracker.html
│       ├── models/
│       │   ├── gluon_models.html
│       │   ├── index.html
│       │   ├── keras_layers.html
│       │   ├── keras_models.html
│       │   ├── torch_layers.html
│       │   └── torch_models.html
│       ├── preprocessing/
│       │   ├── image.html
│       │   └── index.html
│       ├── tracker/
│       │   ├── full_tracker.html
│       │   ├── homography_estimator.html
│       │   ├── index.html
│       │   └── player_ball_tracker.html
│       ├── trainer/
│       │   ├── homography_train.html
│       │   ├── index.html
│       │   ├── keypoints_train.html
│       │   └── tracker_train.html
│       └── utils/
│           ├── data.html
│           ├── google_football_utils.html
│           ├── homography.html
│           ├── image.html
│           ├── index.html
│           ├── linker.html
│           ├── masks.html
│           ├── tracker.html
│           ├── utils.html
│           └── vizualization.html
├── models_examples.ipynb
├── narya/
│   ├── README.md
│   ├── __init__.py
│   ├── analytics/
│   │   ├── __init__.py
│   │   └── edg_agent.py
│   ├── datasets/
│   │   ├── __init__.py
│   │   ├── homography_dataset.py
│   │   ├── keypoints_dataset.py
│   │   └── tracking_dataset.py
│   ├── linker/
│   │   ├── __init__.py
│   │   ├── basetrack.py
│   │   ├── kalman_filter.py
│   │   ├── matching.py
│   │   └── multitracker.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── gluon_models.py
│   │   ├── keras_layers.py
│   │   ├── keras_models.py
│   │   ├── torch_layers.py
│   │   └── torch_models.py
│   ├── preprocessing/
│   │   ├── __init__.py
│   │   └── image.py
│   ├── requirements.txt
│   ├── tracker/
│   │   ├── __init__.py
│   │   ├── full_tracker.py
│   │   ├── homography_estimator.py
│   │   └── player_ball_tracker.py
│   ├── trainer/
│   │   ├── __init__.py
│   │   ├── homography_train.py
│   │   ├── keypoints_train.py
│   │   └── tracker_train.py
│   └── utils/
│       ├── __init__.py
│       ├── data.py
│       ├── google_football_utils.py
│       ├── homography.py
│       ├── image.py
│       ├── linker.py
│       ├── masks.py
│       ├── tracker.py
│       ├── utils.py
│       └── vizualization.py
├── requirements.txt
└── training.ipynb
Download .txt
SYMBOL INDEX (228 symbols across 27 files)

FILE: narya/analytics/edg_agent.py
  class AgentValue (line 19) | class AgentValue:
    method __init__ (line 26) | def __init__(
    method get_value (line 44) | def get_value(self, observations):
    method get_edg_map (line 65) | def get_edg_map(

FILE: narya/datasets/homography_dataset.py
  function new_tf_targets (line 27) | def new_tf_targets(self):
  class HorizontalFlipWithHomo (line 48) | class HorizontalFlipWithHomo(A.HorizontalFlip):
    method apply_to_homo (line 53) | def apply_to_homo(self, homo, **params):
  class Lambda (line 57) | class Lambda(A.Lambda):
    method apply_to_homo (line 62) | def apply_to_homo(self, homo, **params):
  class RandomCropWithHomo (line 66) | class RandomCropWithHomo(A.RandomCrop):
    method apply_to_homo (line 71) | def apply_to_homo(self, homo, **params):
  class Dataset (line 75) | class Dataset:
    method __init__ (line 88) | def __init__(self, images_dir, homo_dir, augmentation=None, preprocess...
    method __len__ (line 99) | def __len__(self):
    method __getitem__ (line 102) | def __getitem__(self, i):
  function get_training_augmentation (line 133) | def get_training_augmentation():
  function get_validation_augmentation (line 162) | def get_validation_augmentation():
  function get_preprocessing (line 179) | def get_preprocessing(preprocessing_fn):
  class Dataloder (line 196) | class Dataloder(tf.keras.utils.Sequence):
    method __init__ (line 205) | def __init__(self, dataset, batch_size=1, shuffle=False):
    method __getitem__ (line 213) | def __getitem__(self, i):
    method __len__ (line 233) | def __len__(self):
    method on_epoch_end (line 237) | def on_epoch_end(self):
  class HomographyDatasetBuilder (line 243) | class HomographyDatasetBuilder:
    method __init__ (line 259) | def __init__(
    method _get_dataset (line 291) | def _get_dataset(self):
    method _get_dataloader (line 294) | def _get_dataloader(self):

FILE: narya/datasets/keypoints_dataset.py
  class Dataset (line 22) | class Dataset:
    method __init__ (line 36) | def __init__(
    method __getitem__ (line 57) | def __getitem__(self, i):
    method __len__ (line 96) | def __len__(self):
  function get_training_augmentation (line 100) | def get_training_augmentation():
  function get_preprocessing (line 128) | def get_preprocessing(preprocessing_fn):
  class Dataloder (line 145) | class Dataloder(keras.utils.Sequence):
    method __init__ (line 154) | def __init__(self, dataset, batch_size=1, shuffle=False):
    method __getitem__ (line 162) | def __getitem__(self, i):
    method __len__ (line 176) | def __len__(self):
    method on_epoch_end (line 180) | def on_epoch_end(self):
  class KeyPointDatasetBuilder (line 186) | class KeyPointDatasetBuilder:
    method __init__ (line 202) | def __init__(
    method _get_dataset (line 235) | def _get_dataset(self):
    method _get_dataloader (line 238) | def _get_dataloader(self):

FILE: narya/datasets/tracking_dataset.py
  class VOCFootball (line 26) | class VOCFootball(VOCDetection):
    method __init__ (line 46) | def __init__(
  function get_dataloader (line 55) | def get_dataloader(
  class TrackingDatasetBuilder (line 110) | class TrackingDatasetBuilder:
    method __init__ (line 125) | def __init__(
    method _get_dataset (line 153) | def _get_dataset(self):
    method _get_dataloader (line 156) | def _get_dataloader(self):

FILE: narya/linker/basetrack.py
  class TrackState (line 15) | class TrackState(object):
  class BaseTrack (line 22) | class BaseTrack(object):
    method end_frame (line 41) | def end_frame(self):
    method next_id (line 45) | def next_id():
    method activate (line 49) | def activate(self, *args):
    method predict (line 52) | def predict(self):
    method update (line 55) | def update(self, *args, **kwargs):
    method mark_lost (line 58) | def mark_lost(self):
    method mark_removed (line 61) | def mark_removed(self):

FILE: narya/linker/kalman_filter.py
  class KalmanFilter (line 32) | class KalmanFilter(object):
    method __init__ (line 44) | def __init__(self):
    method initiate (line 59) | def initiate(self, measurement):
    method predict (line 88) | def predict(self, mean, covariance):
    method project (line 123) | def project(self, mean, covariance):
    method multi_predict (line 147) | def multi_predict(self, mean, covariance):
    method update (line 184) | def update(self, mean, covariance, measurement):
    method gating_distance (line 214) | def gating_distance(

FILE: narya/linker/matching.py
  function linear_assignment (line 16) | def linear_assignment(cost_matrix, thresh):
  function ious (line 43) | def ious(atlbrs, btlbrs):
  function iou_distance (line 64) | def iou_distance(atracks, btracks):
  function embedding_distance (line 88) | def embedding_distance(tracks, detections, metric="cosine"):
  function gate_cost_matrix (line 110) | def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=...
  function fuse_motion (line 134) | def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False...

FILE: narya/linker/multitracker.py
  class STrack (line 41) | class STrack(BaseTrack):
    method __init__ (line 44) | def __init__(self, tlwh, score, temp_feat, buffer_size=30):
    method update_features (line 60) | def update_features(self, feat):
    method predict (line 70) | def predict(self):
    method multi_predict (line 79) | def multi_predict(stracks):
    method activate (line 93) | def activate(self, kalman_filter, frame_id):
    method re_activate (line 109) | def re_activate(self, new_track, frame_id, new_id=False):
    method update (line 122) | def update(self, new_track, frame_id, update_feature=True):
    method tlwh (line 146) | def tlwh(self):
    method tlbr (line 159) | def tlbr(self):
    method tlwh_to_xyah (line 169) | def tlwh_to_xyah(tlwh):
    method to_xyah (line 178) | def to_xyah(self):
    method tlbr_to_tlwh (line 183) | def tlbr_to_tlwh(tlbr):
    method tlwh_to_tlbr (line 190) | def tlwh_to_tlbr(tlwh):
    method __repr__ (line 195) | def __repr__(self):
  class JDETracker (line 199) | class JDETracker(object):
    method __init__ (line 200) | def __init__(
    method update (line 232) | def update(self, im_blob, img0, split_size=None, verbose=True):
  function joint_stracks (line 412) | def joint_stracks(tlista, tlistb):
  function sub_stracks (line 426) | def sub_stracks(tlista, tlistb):
  function remove_duplicate_stracks (line 437) | def remove_duplicate_stracks(stracksa, stracksb):

FILE: narya/models/gluon_models.py
  class TrackerModel (line 14) | class TrackerModel:
    method __init__ (line 31) | def __init__(
    method __call__ (line 56) | def __call__(self, input_img, split_size=None):
    method load_weights (line 95) | def load_weights(self, weights_path):

FILE: narya/models/keras_layers.py
  function pyramid_layer (line 9) | def pyramid_layer(

FILE: narya/models/keras_models.py
  function _build_resnet18 (line 21) | def _build_resnet18():
  class DeepHomoModel (line 45) | class DeepHomoModel:
    method __init__ (line 56) | def __init__(self, pretrained=False, input_shape=(256, 256)):
    method __call__ (line 73) | def __call__(self, input_img):
    method load_weights (line 80) | def load_weights(self, weights_path):
  class KeypointDetectorModel (line 93) | class KeypointDetectorModel:
    method __init__ (line 106) | def __init__(
    method __call__ (line 135) | def __call__(self, input_img):
    method load_weights (line 141) | def load_weights(self, weights_path):

FILE: narya/models/torch_layers.py
  function weights_init_kaiming (line 12) | def weights_init_kaiming(m):
  function weights_init_classifier (line 28) | def weights_init_classifier(m):
  class ClassBlock (line 36) | class ClassBlock(nn.Module):
    method __init__ (line 52) | def __init__(
    method forward (line 87) | def forward(self, x):

FILE: narya/models/torch_models.py
  class ft_net (line 18) | class ft_net(nn.Module):
    method __init__ (line 29) | def __init__(self, class_num, droprate=0.5, stride=2, pretrained=False):
    method forward (line 43) | def forward(self, x):
  class ReIdModel (line 58) | class ReIdModel:
    method __init__ (line 70) | def __init__(self, pretrained=False, input_shape=(256, 128), class_num...
    method __call__ (line 78) | def __call__(self, input_img):
    method load_weights (line 85) | def load_weights(self, weights_path):
    method _get_embeddings (line 99) | def _get_embeddings(self, image, bbox, score, cid, score_tresh, input_...

FILE: narya/preprocessing/image.py
  function _build_reid_preprocessing (line 24) | def _build_reid_preprocessing(input_shape):
  function _build_keypoint_preprocessing (line 52) | def _build_keypoint_preprocessing(input_shape, backbone):
  function _build_tracking_preprocessing (line 77) | def _build_tracking_preprocessing(input_shape):
  function _build_homo_preprocessing (line 101) | def _build_homo_preprocessing(input_shape):

FILE: narya/tracker/full_tracker.py
  class FootballTracker (line 18) | class FootballTracker:
    method __init__ (line 52) | def __init__(
    method __call__ (line 78) | def __call__(

FILE: narya/tracker/homography_estimator.py
  class HomographyEstimator (line 25) | class HomographyEstimator:
    method __init__ (line 38) | def __init__(
    method __call__ (line 74) | def __call__(self, input_img):
    method get_field_coordinates (line 86) | def get_field_coordinates(self, bbox, pred_homo, method):

FILE: narya/tracker/player_ball_tracker.py
  class PlayerBallTracker (line 23) | class PlayerBallTracker:
    method __init__ (line 33) | def __init__(self, conf_tresh=0.5, track_buffer=30, K=100, frame_rate=...
    method get_tracking (line 40) | def get_tracking(

FILE: narya/trainer/tracker_train.py
  function save_params (line 66) | def save_params(net, best_map, current_map, epoch, save_interval, prefix):
  function validate (line 77) | def validate(net, val_data, ctx, eval_metric):
  function train (line 117) | def train(net, train_data, val_data, eval_metric, ctx):

FILE: narya/utils/data.py
  function _parse_xml_file_keypoints (line 9) | def _parse_xml_file_keypoints(xml_file, max_size=320):
  function load_dump (line 32) | def load_dump(dump_file):

FILE: narya/utils/google_football_utils.py
  function _get_values (line 33) | def _get_values():
  function mark_points (line 36) | def mark_points(frame, frame_cnt, points):
  function _left_parser (line 61) | def _left_parser(tab, i):
  function _right_parser (line 72) | def _right_parser(tab, i):
  function _ball_parser (line 83) | def _ball_parser(tab, i):
  function _active_parser (line 90) | def _active_parser(tab, i):
  function _build_obs_stacked (line 104) | def _build_obs_stacked(tab, i):
  function _add_ball_coordinates (line 140) | def _add_ball_coordinates(dataframe: pd.DataFrame, id_ball=-1) -> pd.Dat...
  function _add_possession (line 181) | def _add_possession(dataframe: pd.DataFrame, indx_color=4) -> pd.DataFrame:
  function _prepare_dataset (line 205) | def _prepare_dataset(
  function _scale_mapper (line 243) | def _scale_mapper(x, y):
  function _save_data (line 257) | def _save_data(df, filename):
  function _reverse_points (line 343) | def _reverse_points(x, y):
  function _change (line 356) | def _change(observation, observation_count, x, y, new_x, new_y):
  function change (line 373) | def change(observation, observation_count, x, y, new_x, new_y, entity):
  function _change_random (line 391) | def _change_random(observation, observation_count, x, y):
  function _add_noise (line 405) | def _add_noise(observation, observation_count, x, y, x_std=5, y_std=5):
  function traverse (line 420) | def traverse(observation, observation_count, x, y, entity):

FILE: narya/utils/homography.py
  function normalize_homo (line 21) | def normalize_homo(h, **kwargs):
  function horizontal_flip_homo (line 34) | def horizontal_flip_homo(h, **kwargs):
  function vertical_flip_homo (line 48) | def vertical_flip_homo(h, **kwargs):
  function get_perspective_transform_torch (line 62) | def get_perspective_transform_torch(src, dst):
  function get_perspective_transform_cv (line 76) | def get_perspective_transform_cv(src, dst):
  function get_perspective_transform (line 97) | def get_perspective_transform(src, dst, method="cv"):
  function warp_image (line 116) | def warp_image(img, H, out_shape=None, method="cv"):
  function warp_image_torch (line 136) | def warp_image_torch(img, H, out_shape=None):
  function warp_image_cv (line 205) | def warp_image_cv(img, H, out_shape=None):
  function warp_point (line 234) | def warp_point(pts, homography, method="cv"):
  function warp_point_cv (line 242) | def warp_point_cv(pts, homography):
  function warp_point_torch (line 247) | def warp_point_torch(pts, homography, input_shape = (320,320,3)):
  function get_default_corners (line 267) | def get_default_corners(batch_size):
  function get_corners_from_nn (line 284) | def get_corners_from_nn(batch_corners_pred):
  function compute_homography (line 301) | def compute_homography(batch_corners_pred):
  function get_four_corners (line 320) | def get_four_corners(homo_mat):

FILE: narya/utils/image.py
  function torch_img_to_np_img (line 13) | def torch_img_to_np_img(torch_img):
  function np_img_to_torch_img (line 42) | def np_img_to_torch_img(np_img):
  function normalize_single_image_torch (line 67) | def normalize_single_image_torch(image, img_mean=None, img_std=None):
  function denormalize (line 95) | def denormalize(x):

FILE: narya/utils/linker.py
  function ctdet_post_process (line 17) | def ctdet_post_process(dets, c, s, h, w, num_classes):
  function transform_preds (line 39) | def transform_preds(coords, center, scale, output_size):
  function get_affine_transform (line 47) | def get_affine_transform(
  function affine_transform (line 80) | def affine_transform(pt, t):
  function get_3rd_point (line 86) | def get_3rd_point(a, b):
  function get_dir (line 91) | def get_dir(src_point, rot_rad):
  function xyxy2xywh (line 101) | def xyxy2xywh(x):
  function xywh2xyxy (line 111) | def xywh2xyxy(x):
  function tlbr_to_tlwh (line 121) | def tlbr_to_tlwh(tlbr):
  function tlwh_to_tlbr (line 127) | def tlwh_to_tlbr(tlwh):

FILE: narya/utils/masks.py
  function _get_flip_mapper (line 41) | def _get_flip_mapper():
  function _get_init_homo_mapper (line 78) | def _get_init_homo_mapper():
  function _flip_keypoint (line 82) | def _flip_keypoint(id_kp, x_kp, y_kp, input_shape=(320, 320, 3)):
  function _add_mask (line 109) | def _add_mask(mask, val, x, y):
  function _build_mask (line 130) | def _build_mask(keypoints, mask_shape=(320, 320), nb_of_mask=29):
  function _get_keypoints_from_mask (line 148) | def _get_keypoints_from_mask(mask, treshold=0.9):
  function collinear (line 175) | def collinear(p0, p1, p2, epsilon=0.001):
  function _points_from_mask (line 180) | def _points_from_mask(mask, treshold=0.9):

FILE: narya/utils/tracker.py
  function add_nan_trajectories (line 10) | def add_nan_trajectories(trajectories, max_frame):
  function get_trajectory_from_id (line 43) | def get_trajectory_from_id(trajectories, id_):
  function build_df_per_id (line 73) | def build_df_per_id(trajectories):
  function fill_nan_trajectories (line 94) | def fill_nan_trajectories(df_per_id,window_size=21):
  function get_full_results (line 119) | def get_full_results(df_per_id):
  function _get_max_id (line 138) | def _get_max_id(traj):
  function _remove_coords (line 144) | def _remove_coords(traj, ids, frame):
  function _remove_ids (line 162) | def _remove_ids(traj, list_ids):
  function add_entity (line 176) | def add_entity(traj, entity_id, entity_traj):
  function add_entity_coords (line 193) | def add_entity_coords(traj, entity_id, entity_traj, max_frame):
  function merge_id (line 224) | def merge_id(traj, list_ids_frame):
  function merge_2_trajectories (line 249) | def merge_2_trajectories(traj1, traj2, id_mapper, max_frame_traj1):

FILE: narya/utils/utils.py
  function isnan (line 8) | def isnan(x):
  function hasnan (line 21) | def hasnan(x):
  function round_clip_0_1 (line 34) | def round_clip_0_1(x, **kwargs):
  function to_numpy (line 47) | def to_numpy(var):
  function to_torch (line 63) | def to_torch(np_array):

FILE: narya/utils/vizualization.py
  function visualize (line 48) | def visualize(**images):
  function draw_pitch (line 69) | def draw_pitch(dpi=100, pitch_color="#a8bc95"):
  function draw_patches (line 100) | def draw_patches(axes):
  function draw_frame (line 168) | def draw_frame(
  function add_voronoi_to_fig (line 279) | def add_voronoi_to_fig(fig, ax, dfFrame):
  function calculate_voronoi (line 313) | def calculate_voronoi(dfFrame):
  function get_frame (line 341) | def get_frame(df, t, fps=20):
  function draw_frame_x (line 356) | def draw_frame_x(df, t, fps, voronoi=True):
  function make_animation (line 375) | def make_animation(df, fps=20, voronoi=True):
  function draw_line (line 393) | def draw_line(df_value, t, fps, smooth=True, show=True):
  function make_animation_value (line 439) | def make_animation_value(df, fps=20):
  function add_edg_to_fig (line 456) | def add_edg_to_fig(fig, ax, edg_map, vmin=None, vmax=None):
  function get_color (line 487) | def get_color(idx):
  function plot_tracking (line 494) | def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0....
  function rgb_template_to_coord_conv_template (line 537) | def rgb_template_to_coord_conv_template(rgb_template):
  function merge_template (line 550) | def merge_template(img, warped_template):
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]

About this extraction

This page contains the full source code of the DonsetPG/narya GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 90 files (25.2 MB), approximately 6.6M tokens, and a symbol index with 228 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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